@inproceedings{arthur-etal-2026-nlpgroup8,
title = "{NLPG}roup8 at {S}em{E}val-2026 Task 2: Diverse Ensembles and Hierarchical Transformers for Emotional State Prediction",
author = "Arthur, Troy and
Kelley, Aidan and
Reschke, Sierra",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.56/",
pages = "379--386",
ISBN = "979-8-89176-414-9",
abstract = "Our approach combines a diverse ensemble for Subtask 1 with a context-aware transformer aggregation architecture for temporal forecasting in Subtasks 2A and 2B. The ensemble achieved state-of-the-art performance for the Subtask 1 Valence metric, ranking first in Valence prediction. Our Subtask 2B independent architecture ranked second in Valence prediction and fourth in Arousal prediction among competitive submissions. We also report results for Subtask 2A, analyzing challenges our architecture faced with next-entry affect forecasting. These findings underscore the significance of our methodology for affective prediction, achieved without reliance on external affective datasets."
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<abstract>Our approach combines a diverse ensemble for Subtask 1 with a context-aware transformer aggregation architecture for temporal forecasting in Subtasks 2A and 2B. The ensemble achieved state-of-the-art performance for the Subtask 1 Valence metric, ranking first in Valence prediction. Our Subtask 2B independent architecture ranked second in Valence prediction and fourth in Arousal prediction among competitive submissions. We also report results for Subtask 2A, analyzing challenges our architecture faced with next-entry affect forecasting. These findings underscore the significance of our methodology for affective prediction, achieved without reliance on external affective datasets.</abstract>
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%0 Conference Proceedings
%T NLPGroup8 at SemEval-2026 Task 2: Diverse Ensembles and Hierarchical Transformers for Emotional State Prediction
%A Arthur, Troy
%A Kelley, Aidan
%A Reschke, Sierra
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F arthur-etal-2026-nlpgroup8
%X Our approach combines a diverse ensemble for Subtask 1 with a context-aware transformer aggregation architecture for temporal forecasting in Subtasks 2A and 2B. The ensemble achieved state-of-the-art performance for the Subtask 1 Valence metric, ranking first in Valence prediction. Our Subtask 2B independent architecture ranked second in Valence prediction and fourth in Arousal prediction among competitive submissions. We also report results for Subtask 2A, analyzing challenges our architecture faced with next-entry affect forecasting. These findings underscore the significance of our methodology for affective prediction, achieved without reliance on external affective datasets.
%U https://aclanthology.org/2026.semeval-1.56/
%P 379-386
Markdown (Informal)
[NLPGroup8 at SemEval-2026 Task 2: Diverse Ensembles and Hierarchical Transformers for Emotional State Prediction](https://aclanthology.org/2026.semeval-1.56/) (Arthur et al., SemEval 2026)
ACL